Overview

Dataset statistics

Number of variables12
Number of observations188
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.8 KiB
Average record size in memory96.7 B

Variable types

Categorical1
Numeric11

Alerts

Date has a high cardinality: 188 distinct valuesHigh cardinality
Confirmed is highly overall correlated with Deaths and 7 other fieldsHigh correlation
Deaths is highly overall correlated with Confirmed and 7 other fieldsHigh correlation
Recovered is highly overall correlated with Confirmed and 7 other fieldsHigh correlation
Active is highly overall correlated with Confirmed and 7 other fieldsHigh correlation
New cases is highly overall correlated with Confirmed and 7 other fieldsHigh correlation
New deaths is highly overall correlated with Confirmed and 7 other fieldsHigh correlation
New recovered is highly overall correlated with Confirmed and 7 other fieldsHigh correlation
Deaths / 100 Cases is highly overall correlated with New deaths and 1 other fieldsHigh correlation
Recovered / 100 Cases is highly overall correlated with Confirmed and 7 other fieldsHigh correlation
Deaths / 100 Recovered is highly overall correlated with Recovered / 100 CasesHigh correlation
No. of countries is highly overall correlated with Confirmed and 8 other fieldsHigh correlation
Date is uniformly distributedUniform
Date has unique valuesUnique
Confirmed has unique valuesUnique
Deaths has unique valuesUnique
Recovered has unique valuesUnique
Active has unique valuesUnique
New cases has unique valuesUnique
New recovered has unique valuesUnique

Reproduction

Analysis started2023-07-08 10:42:22.296033
Analysis finished2023-07-08 10:42:33.553392
Duration11.26 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Date
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct188
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2020-01-22
 
1
2020-05-30
 
1
2020-05-21
 
1
2020-05-22
 
1
2020-05-23
 
1
Other values (183)
183 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1880
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique188 ?
Unique (%)100.0%

Sample

1st row2020-01-22
2nd row2020-01-23
3rd row2020-01-24
4th row2020-01-25
5th row2020-01-26

Common Values

ValueCountFrequency (%)
2020-01-22 1
 
0.5%
2020-05-30 1
 
0.5%
2020-05-21 1
 
0.5%
2020-05-22 1
 
0.5%
2020-05-23 1
 
0.5%
2020-05-24 1
 
0.5%
2020-05-25 1
 
0.5%
2020-05-26 1
 
0.5%
2020-05-27 1
 
0.5%
2020-05-28 1
 
0.5%
Other values (178) 178
94.7%

Length

2023-07-08T16:12:33.628787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-01-22 1
 
0.5%
2020-02-01 1
 
0.5%
2020-02-12 1
 
0.5%
2020-01-24 1
 
0.5%
2020-01-25 1
 
0.5%
2020-01-26 1
 
0.5%
2020-01-27 1
 
0.5%
2020-01-28 1
 
0.5%
2020-01-29 1
 
0.5%
2020-01-30 1
 
0.5%
Other values (178) 178
94.7%

Most occurring characters

ValueCountFrequency (%)
0 635
33.8%
2 490
26.1%
- 376
20.0%
1 91
 
4.8%
3 58
 
3.1%
5 50
 
2.7%
4 49
 
2.6%
6 49
 
2.6%
7 46
 
2.4%
8 18
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1504
80.0%
Dash Punctuation 376
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 635
42.2%
2 490
32.6%
1 91
 
6.1%
3 58
 
3.9%
5 50
 
3.3%
4 49
 
3.3%
6 49
 
3.3%
7 46
 
3.1%
8 18
 
1.2%
9 18
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 376
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1880
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 635
33.8%
2 490
26.1%
- 376
20.0%
1 91
 
4.8%
3 58
 
3.1%
5 50
 
2.7%
4 49
 
2.6%
6 49
 
2.6%
7 46
 
2.4%
8 18
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1880
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 635
33.8%
2 490
26.1%
- 376
20.0%
1 91
 
4.8%
3 58
 
3.1%
5 50
 
2.7%
4 49
 
2.6%
6 49
 
2.6%
7 46
 
2.4%
8 18
 
1.0%

Confirmed
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct188
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4406960
Minimum555
Maximum16480485
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-07-08T16:12:33.718787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum555
5-th percentile10665.85
Q1112191
median2848733
Q37422045.5
95-th percentile14209026
Maximum16480485
Range16479930
Interquartile range (IQR)7309854.5

Descriptive statistics

Standard deviation4757988.3
Coefficient of variation (CV)1.0796532
Kurtosis-0.284262
Mean4406960
Median Absolute Deviation (MAD)2770604.5
Skewness0.92806972
Sum8.2850848 × 108
Variance2.2638453 × 1013
MonotonicityStrictly increasing
2023-07-08T16:12:33.813690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
555 1
 
0.5%
6077978 1
 
0.5%
5110064 1
 
0.5%
5216964 1
 
0.5%
5322253 1
 
0.5%
5417579 1
 
0.5%
5504542 1
 
0.5%
5597064 1
 
0.5%
5699664 1
 
0.5%
5818978 1
 
0.5%
Other values (178) 178
94.7%
ValueCountFrequency (%)
555 1
0.5%
654 1
0.5%
941 1
0.5%
1434 1
0.5%
2118 1
0.5%
2927 1
0.5%
5578 1
0.5%
6166 1
0.5%
8234 1
0.5%
9927 1
0.5%
ValueCountFrequency (%)
16480485 1
0.5%
16251796 1
0.5%
16047190 1
0.5%
15791645 1
0.5%
15510481 1
0.5%
15227725 1
0.5%
14947078 1
0.5%
14713623 1
0.5%
14506845 1
0.5%
14292198 1
0.5%

Deaths
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct188
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean230770.76
Minimum17
Maximum654036
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-07-08T16:12:33.922393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile229.1
Q13935
median204190
Q3418634.5
95-th percentile600160.55
Maximum654036
Range654019
Interquartile range (IQR)414699.5

Descriptive statistics

Standard deviation217929.09
Coefficient of variation (CV)0.94435315
Kurtosis-1.311584
Mean230770.76
Median Absolute Deviation (MAD)200792
Skewness0.37859407
Sum43384903
Variance4.749309 × 1010
MonotonicityStrictly increasing
2023-07-08T16:12:34.016491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 1
 
0.5%
370718 1
 
0.5%
334112 1
 
0.5%
339396 1
 
0.5%
343385 1
 
0.5%
346525 1
 
0.5%
347703 1
 
0.5%
351906 1
 
0.5%
357119 1
 
0.5%
361820 1
 
0.5%
Other values (178) 178
94.7%
ValueCountFrequency (%)
17 1
0.5%
18 1
0.5%
26 1
0.5%
42 1
0.5%
56 1
0.5%
82 1
0.5%
131 1
0.5%
133 1
0.5%
171 1
0.5%
213 1
0.5%
ValueCountFrequency (%)
654036 1
0.5%
648621 1
0.5%
644517 1
0.5%
639650 1
0.5%
633506 1
0.5%
623540 1
0.5%
616557 1
0.5%
610319 1
0.5%
606159 1
0.5%
602130 1
0.5%

Recovered
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct188
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2066001.2
Minimum28
Maximum9468087
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-07-08T16:12:34.130107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile240.7
Q160441.25
median784784
Q33416395.8
95-th percentile7891773.5
Maximum9468087
Range9468059
Interquartile range (IQR)3355954.5

Descriptive statistics

Standard deviation2627976.4
Coefficient of variation (CV)1.2720111
Kurtosis0.45482146
Mean2066001.2
Median Absolute Deviation (MAD)780718.5
Skewness1.2664063
Sum3.8840823 × 108
Variance6.9062599 × 1012
MonotonicityStrictly increasing
2023-07-08T16:12:34.234794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 1
 
0.5%
2509981 1
 
0.5%
1900768 1
 
0.5%
2008541 1
 
0.5%
2062802 1
 
0.5%
2117555 1
 
0.5%
2180605 1
 
0.5%
2235118 1
 
0.5%
2297613 1
 
0.5%
2363746 1
 
0.5%
Other values (178) 178
94.7%
ValueCountFrequency (%)
28 1
0.5%
30 1
0.5%
36 1
0.5%
39 1
0.5%
52 1
0.5%
61 1
0.5%
107 1
0.5%
125 1
0.5%
141 1
0.5%
219 1
0.5%
ValueCountFrequency (%)
9468087 1
0.5%
9293464 1
0.5%
9158743 1
0.5%
8939705 1
0.5%
8710969 1
0.5%
8541255 1
0.5%
8364986 1
0.5%
8190777 1
0.5%
8032235 1
0.5%
7944550 1
0.5%

Active
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct188
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2110188
Minimum510
Maximum6358362
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-07-08T16:12:34.343363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum510
5-th percentile10196.05
Q158641.75
median1859759
Q33587015.2
95-th percentile5717091.7
Maximum6358362
Range6357852
Interquartile range (IQR)3528373.5

Descriptive statistics

Standard deviation1969670.4
Coefficient of variation (CV)0.93340992
Kurtosis-1.0021928
Mean2110188
Median Absolute Deviation (MAD)1800910
Skewness0.50420736
Sum3.9671535 × 108
Variance3.8796017 × 1012
MonotonicityNot monotonic
2023-07-08T16:12:34.460846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
510 1
 
0.5%
3197279 1
 
0.5%
2875184 1
 
0.5%
2869027 1
 
0.5%
2916066 1
 
0.5%
2953499 1
 
0.5%
2976234 1
 
0.5%
3010040 1
 
0.5%
3044932 1
 
0.5%
3093412 1
 
0.5%
Other values (178) 178
94.7%
ValueCountFrequency (%)
510 1
0.5%
606 1
0.5%
879 1
0.5%
1353 1
0.5%
2010 1
0.5%
2784 1
0.5%
5340 1
0.5%
5908 1
0.5%
7922 1
0.5%
9495 1
0.5%
ValueCountFrequency (%)
6358362 1
0.5%
6309711 1
0.5%
6243930 1
0.5%
6212290 1
0.5%
6166006 1
0.5%
6062930 1
0.5%
5965535 1
0.5%
5912527 1
0.5%
5868451 1
0.5%
5745518 1
0.5%

New cases
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct188
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87771.021
Minimum0
Maximum282756
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-07-08T16:12:34.565362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile648.25
Q15568.5
median81114
Q3131502.5
95-th percentile230271.85
Maximum282756
Range282756
Interquartile range (IQR)125934

Descriptive statistics

Standard deviation75295.293
Coefficient of variation (CV)0.85786051
Kurtosis-0.4123708
Mean87771.021
Median Absolute Deviation (MAD)61133.5
Skewness0.62712651
Sum16500952
Variance5.6693812 × 109
MonotonicityNot monotonic
2023-07-08T16:12:34.661894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.5%
137833 1
 
0.5%
106438 1
 
0.5%
106900 1
 
0.5%
105289 1
 
0.5%
95326 1
 
0.5%
87335 1
 
0.5%
92742 1
 
0.5%
102600 1
 
0.5%
119314 1
 
0.5%
Other values (178) 178
94.7%
ValueCountFrequency (%)
0 1
0.5%
99 1
0.5%
287 1
0.5%
323 1
0.5%
421 1
0.5%
493 1
0.5%
547 1
0.5%
564 1
0.5%
588 1
0.5%
629 1
0.5%
ValueCountFrequency (%)
282756 1
0.5%
281164 1
0.5%
280647 1
0.5%
255545 1
0.5%
252544 1
0.5%
242038 1
0.5%
237635 1
0.5%
233565 1
0.5%
232577 1
0.5%
231122 1
0.5%

New deaths
Real number (ℝ)

Distinct185
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3478.8245
Minimum0
Maximum9966
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-07-08T16:12:34.751430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30.2
Q1250.75
median4116
Q35346
95-th percentile6887.15
Maximum9966
Range9966
Interquartile range (IQR)5095.25

Descriptive statistics

Standard deviation2537.7357
Coefficient of variation (CV)0.7294808
Kurtosis-1.1664463
Mean3478.8245
Median Absolute Deviation (MAD)1740
Skewness-0.12616544
Sum654019
Variance6440102.2
MonotonicityNot monotonic
2023-07-08T16:12:34.841068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5311 2
 
1.1%
3473 2
 
1.1%
100 2
 
1.1%
4885 1
 
0.5%
5284 1
 
0.5%
3989 1
 
0.5%
3140 1
 
0.5%
1178 1
 
0.5%
4203 1
 
0.5%
5213 1
 
0.5%
Other values (175) 175
93.1%
ValueCountFrequency (%)
0 1
0.5%
1 1
0.5%
2 1
0.5%
4 1
0.5%
5 1
0.5%
8 1
0.5%
10 1
0.5%
14 1
0.5%
16 1
0.5%
26 1
0.5%
ValueCountFrequency (%)
9966 1
0.5%
8890 1
0.5%
8312 1
0.5%
7902 1
0.5%
7629 1
0.5%
7283 1
0.5%
7272 1
0.5%
7157 1
0.5%
6983 1
0.5%
6898 1
0.5%

New recovered
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct188
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50362.016
Minimum0
Maximum284394
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-07-08T16:12:34.939139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67.6
Q12488.25
median30991.5
Q379706.25
95-th percentile159114.65
Maximum284394
Range284394
Interquartile range (IQR)77218

Descriptive statistics

Standard deviation56090.892
Coefficient of variation (CV)1.1137539
Kurtosis1.4714015
Mean50362.016
Median Absolute Deviation (MAD)29331.5
Skewness1.3038237
Sum9468059
Variance3.1461882 × 109
MonotonicityNot monotonic
2023-07-08T16:12:35.028746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.5%
69854 1
 
0.5%
50327 1
 
0.5%
107773 1
 
0.5%
54261 1
 
0.5%
54753 1
 
0.5%
63050 1
 
0.5%
54513 1
 
0.5%
62495 1
 
0.5%
66133 1
 
0.5%
Other values (178) 178
94.7%
ValueCountFrequency (%)
0 1
0.5%
2 1
0.5%
3 1
0.5%
6 1
0.5%
9 1
0.5%
13 1
0.5%
16 1
0.5%
18 1
0.5%
46 1
0.5%
62 1
0.5%
ValueCountFrequency (%)
284394 1
0.5%
228736 1
0.5%
219038 1
0.5%
195433 1
0.5%
176269 1
0.5%
174623 1
0.5%
174209 1
0.5%
169714 1
0.5%
159519 1
0.5%
159423 1
0.5%

Deaths / 100 Cases
Real number (ℝ)

Distinct162
Distinct (%)86.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8606383
Minimum2.04
Maximum7.18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-07-08T16:12:35.122929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.04
5-th percentile2.1635
Q13.51
median4.85
Q36.2975
95-th percentile7.11
Maximum7.18
Range5.14
Interquartile range (IQR)2.7875

Descriptive statistics

Standard deviation1.5795409
Coefficient of variation (CV)0.32496573
Kurtosis-1.149222
Mean4.8606383
Median Absolute Deviation (MAD)1.41
Skewness-0.14454005
Sum913.8
Variance2.4949493
MonotonicityNot monotonic
2023-07-08T16:12:35.216666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.43 4
 
2.1%
7.18 3
 
1.6%
3.44 3
 
1.6%
7.05 3
 
1.6%
7.16 2
 
1.1%
4.48 2
 
1.1%
3.41 2
 
1.1%
6.88 2
 
1.1%
4.34 2
 
1.1%
5.28 2
 
1.1%
Other values (152) 163
86.7%
ValueCountFrequency (%)
2.04 1
0.5%
2.06 2
1.1%
2.08 1
0.5%
2.09 1
0.5%
2.14 1
0.5%
2.15 2
1.1%
2.16 2
1.1%
2.17 1
0.5%
2.26 1
0.5%
2.28 2
1.1%
ValueCountFrequency (%)
7.18 3
1.6%
7.16 2
1.1%
7.15 1
 
0.5%
7.14 2
1.1%
7.13 1
 
0.5%
7.11 2
1.1%
7.09 1
 
0.5%
7.07 1
 
0.5%
7.05 3
1.6%
7.04 1
 
0.5%

Recovered / 100 Cases
Real number (ℝ)

Distinct185
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.343936
Minimum1.71
Maximum57.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-07-08T16:12:35.312220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.71
5-th percentile3.18
Q122.785
median35.68
Q348.945
95-th percentile55.422
Maximum57.45
Range55.74
Interquartile range (IQR)26.16

Descriptive statistics

Standard deviation16.206159
Coefficient of variation (CV)0.47187831
Kurtosis-0.8430659
Mean34.343936
Median Absolute Deviation (MAD)13.205
Skewness-0.43128346
Sum6456.66
Variance262.63957
MonotonicityNot monotonic
2023-07-08T16:12:35.397768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.59 2
 
1.1%
20.03 2
 
1.1%
28.1 2
 
1.1%
5.05 1
 
0.5%
41.3 1
 
0.5%
38.76 1
 
0.5%
39.09 1
 
0.5%
39.61 1
 
0.5%
39.93 1
 
0.5%
40.31 1
 
0.5%
Other values (175) 175
93.1%
ValueCountFrequency (%)
1.71 1
0.5%
1.92 1
0.5%
2.03 1
0.5%
2.08 1
0.5%
2.21 1
0.5%
2.33 1
0.5%
2.46 1
0.5%
2.72 1
0.5%
2.73 1
0.5%
3.04 1
0.5%
ValueCountFrequency (%)
57.45 1
0.5%
57.18 1
0.5%
57.07 1
0.5%
56.61 1
0.5%
56.16 1
0.5%
56.09 1
0.5%
55.96 1
0.5%
55.67 1
0.5%
55.59 1
0.5%
55.45 1
0.5%

Deaths / 100 Recovered
Real number (ℝ)

Distinct182
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.104521
Minimum6.26
Maximum134.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-07-08T16:12:35.515376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6.26
5-th percentile6.98
Q19.65
median15.38
Q325.3425
95-th percentile71.6285
Maximum134.43
Range128.17
Interquartile range (IQR)15.6925

Descriptive statistics

Standard deviation22.568307
Coefficient of variation (CV)1.0209815
Kurtosis9.7956168
Mean22.104521
Median Absolute Deviation (MAD)7.105
Skewness3.0422628
Sum4155.65
Variance509.32848
MonotonicityNot monotonic
2023-07-08T16:12:35.618894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.23 2
 
1.1%
107.69 2
 
1.1%
28.76 2
 
1.1%
11.98 2
 
1.1%
6.98 2
 
1.1%
27.28 2
 
1.1%
60.71 1
 
0.5%
15.02 1
 
0.5%
16.65 1
 
0.5%
16.36 1
 
0.5%
Other values (172) 172
91.5%
ValueCountFrequency (%)
6.26 1
0.5%
6.36 1
0.5%
6.4 1
0.5%
6.43 1
0.5%
6.54 1
0.5%
6.55 1
0.5%
6.76 1
0.5%
6.78 1
0.5%
6.91 1
0.5%
6.98 2
1.1%
ValueCountFrequency (%)
134.43 1
0.5%
122.43 1
0.5%
121.28 1
0.5%
107.69 2
1.1%
106.4 1
0.5%
97.26 1
0.5%
92.17 1
0.5%
78.87 1
0.5%
72.22 1
0.5%
70.53 1
0.5%

No. of countries
Real number (ℝ)

Distinct56
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144.35106
Minimum6
Maximum187
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-07-08T16:12:35.707405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile24.35
Q1101.25
median184
Q3187
95-th percentile187
Maximum187
Range181
Interquartile range (IQR)85.75

Descriptive statistics

Standard deviation65.175979
Coefficient of variation (CV)0.45151021
Kurtosis-0.49893616
Mean144.35106
Median Absolute Deviation (MAD)3
Skewness-1.138638
Sum27138
Variance4247.9082
MonotonicityIncreasing
2023-07-08T16:12:35.790922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
187 76
40.4%
184 20
 
10.6%
186 13
 
6.9%
26 10
 
5.3%
27 5
 
2.7%
183 4
 
2.1%
25 3
 
1.6%
176 3
 
1.6%
180 3
 
1.6%
179 2
 
1.1%
Other values (46) 49
26.1%
ValueCountFrequency (%)
6 1
 
0.5%
8 1
 
0.5%
9 1
 
0.5%
11 1
 
0.5%
13 1
 
0.5%
16 2
1.1%
18 1
 
0.5%
20 1
 
0.5%
24 1
 
0.5%
25 3
1.6%
ValueCountFrequency (%)
187 76
40.4%
186 13
 
6.9%
184 20
 
10.6%
183 4
 
2.1%
182 1
 
0.5%
180 3
 
1.6%
179 2
 
1.1%
177 1
 
0.5%
176 3
 
1.6%
175 1
 
0.5%

Interactions

2023-07-08T16:12:32.159700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:22.493236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:23.419672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:24.349696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:25.254920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:26.156317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:27.066301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:28.479335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:29.388150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:30.234516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:31.179031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:32.261302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:22.582934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:23.500643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:24.433825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:25.337539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:26.235986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:27.574183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:28.557981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:29.464754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:30.324002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:31.276670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:32.347866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:22.663514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:23.594727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:24.517428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:25.418257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:26.322669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:27.653929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:28.640715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:29.540291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:30.414054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:31.394343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:32.439467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:22.733234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:23.687383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:24.585499image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:25.496311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:26.403720image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:27.725537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:28.723379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:29.611310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:30.484694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:31.483912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:32.516461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:22.820723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:23.770119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:24.671065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:25.576845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:26.487429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:27.847832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:28.803444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:29.686822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:30.564659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:31.574550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:32.596212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:22.905744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:23.849645image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:24.765655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:25.647569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:26.562044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:27.958389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:28.896993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:29.753349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:30.649505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:31.655272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:32.680884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:22.980308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:23.930349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:24.849173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:25.727774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:26.639543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:28.041571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:28.975547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:29.832295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:30.730124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:31.730794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:32.758304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:23.059157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:24.016983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:24.927846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:25.819426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:26.713521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:28.132488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:29.063205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:29.907314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:30.818127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:31.812235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:32.846778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:23.139633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:24.092984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:25.001921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:25.897381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:26.796348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:28.224733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:29.131995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:29.984928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:30.890179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:31.905828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:32.933515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:23.232306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:24.167730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:25.077428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:25.984885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:26.891891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:28.295735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:29.212989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:30.073472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:30.980984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:31.989312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:33.010525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:23.320897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:24.270196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:25.162143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:26.074618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:26.973579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:28.374536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:29.306661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:30.147005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:31.068775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-07-08T16:12:32.072890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-07-08T16:12:35.877453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ConfirmedDeathsRecoveredActiveNew casesNew deathsNew recoveredDeaths / 100 CasesRecovered / 100 CasesDeaths / 100 RecoveredNo. of countries
Confirmed1.0001.0001.0000.9940.9700.6560.9830.4920.761-0.4930.966
Deaths1.0001.0001.0000.9940.9700.6560.9830.4920.761-0.4930.966
Recovered1.0001.0001.0000.9940.9700.6560.9830.4920.761-0.4930.966
Active0.9940.9940.9941.0000.9670.6570.9770.4860.721-0.4630.960
New cases0.9700.9700.9700.9671.0000.7040.9570.4680.687-0.3990.937
New deaths0.6560.6560.6560.6570.7041.0000.6680.7320.2670.0180.645
New recovered0.9830.9830.9830.9770.9570.6681.0000.4950.743-0.4800.954
Deaths / 100 Cases0.4920.4920.4920.4860.4680.7320.4951.0000.0910.2060.601
Recovered / 100 Cases0.7610.7610.7610.7210.6870.2670.7430.0911.000-0.9090.694
Deaths / 100 Recovered-0.493-0.493-0.493-0.463-0.3990.018-0.4800.206-0.9091.000-0.410
No. of countries0.9660.9660.9660.9600.9370.6450.9540.6010.694-0.4101.000

Missing values

2023-07-08T16:12:33.139800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-08T16:12:33.497472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DateConfirmedDeathsRecoveredActiveNew casesNew deathsNew recoveredDeaths / 100 CasesRecovered / 100 CasesDeaths / 100 RecoveredNo. of countries
02020-01-2255517285100003.065.0560.716
12020-01-23654183060699122.754.5960.008
22020-01-249412636879287862.763.8372.229
32020-01-251434423913534931632.932.72107.6911
42020-01-2621185652201068414132.642.46107.6913
52020-01-272927826127848092692.802.08134.4316
62020-01-2855781311075340265149462.351.92122.4316
72020-01-29616613312559085882182.162.03106.4018
82020-01-3082341711417922206838162.081.71121.2820
92020-01-3199272132199495169342782.152.2197.2624
DateConfirmedDeathsRecoveredActiveNew casesNew deathsNew recoveredDeaths / 100 CasesRecovered / 100 CasesDeaths / 100 RecoveredNo. of countries
1782020-07-18142921986021307944550574551823763556271507904.2155.597.58187
1792020-07-1914506845606159803223558684512146474029876854.1855.377.55187
1802020-07-20147136236103198190777591252720677841601585424.1555.677.45187
1812020-07-21149470786165578364986596553523356562381742094.1255.967.37187
1822020-07-22152277256235408541255606293028064769831762694.0956.097.30187
1832020-07-23155104816335068710969616600628275699661697144.0856.167.27187
1842020-07-24157916456396508939705621229028116461442287364.0556.617.16187
1852020-07-25160471906445179158743624393025554548672190384.0257.077.04187
1862020-07-26162517966486219293464630971120460641041347213.9957.186.98187
1872020-07-27164804856540369468087635836222869354151746233.9757.456.91187